L05 Annotation & Positioning

Data Visualization (STAT 302)

Overview

The goal of this lab is to explore methods for annotating and positioning with ggplot2 plots. This lab also utilizes scale_* to a greater degree which is part of our next reading. In fact, students may find going through/reading chapter 11 Colour scales and legends useful.

Datasets

We’ll be using the blue_jays.rda, titanic.rda, Aus_athletes.rda, and tech_stocks.rda datasets.

Code

# Load package(s)
library(tidyverse)
library(ggrepel)
library(patchwork)
library(cowplot)

# Load data
load("data/Aus_athletes.rda")
load("data/tech_stocks.rda")
load("data/titanic.rda")
load("data/blue_jays.rda")

Exercises

Complete the following exercises.

Exercise 1

Using the blue_jays.rda dataset, recreate the following graphic as precisely as possible.

Hints:

• Transparency is 0.8
• Point size 2
• Create a label_info dataset that is a subset of original data, just with the 2 birds to be labeled
• Shift label text horizontally by 0.5
• See ggplot2 textbook 8.3 building custom annotations
• Annotation size is 4
• Classic theme

Code

label_info <- blue_jays %>%
  filter(BirdID %in% c("702-90567", "1142-05914"))

y_range <- range(blue_jays$Head)
x_range <- range(blue_jays$Mass)

ggplot(blue_jays, aes(Mass, Head, color = KnownSex)) +
  geom_point(alpha = 0.8, size = 2, show.legend = FALSE) +
  theme_classic() +
  geom_text(data = label_info,
            aes(label = KnownSex),
            show.legend = FALSE,
            nudge_x = 0.5) +
  annotate("text", x = x_range[1], y = y_range[2],
           label = "Head length versus body mass for 123 blue jays",
           size = 4,
           hjust = 0,
           vjust = 1) +
  labs(x = "Body mass (g)",
       y = "Head length (mm)")

Exercise 2

Using the tech_stocks dataset, recreate the following graphics as precisely as possible. Use the column price_indexed.

Plot 1

Hints:

• Create a label_info dataset that is a subset of original data, just containing the last day’s information for each of the 4 stocks
• serif font
• Annotation size is 4

Code

label_info <- tech_stocks %>%
  arrange(desc(date)) %>%
  slice_head(n = 4)

xrnge <- range(tech_stocks$date)
yrnge <- range(tech_stocks$price_indexed)

ggplot(tech_stocks, aes(date, price_indexed, color = company, family = "serif")) +
  geom_line(show.legend = FALSE) +
  labs(x = NULL,
       y = "Stock price, indexed") +
  geom_text(data = label_info,
            aes(label = company),
            show.legend = FALSE,
            color = "black",
            family = "serif") +
  annotate("text", xrnge[1], yrnge[2], 
           label = "Stock price over time for four major tech companies",
           vjust = 1, hjust = 0, size = 4, family = "serif") +
  theme_minimal()

Plot 2

Hints:

• Package ggrepel
  • box.padding is 0.6
  • Minimum segment length is 0
  • Horizontal justification is to the right
  • seed of 9876
• Annotation size is 4
• serif font

Code

ggplot(tech_stocks, aes(date, price_indexed, color = company, family = "serif")) +
  geom_line(show.legend = FALSE) +
  labs(x = NULL,
       y = "Stock price, indexed") +
  ggrepel::geom_text_repel(data = label_info,
            aes(label = company),
            show.legend = FALSE,
            color = "black",
            box.padding = 0.6,
            min.segment.length = 0,
            seed = 9876,
            hjust = "right") +
  annotate("text", xrnge[1], yrnge[2], 
           label = "Stock price over time for four major tech companies",
           vjust = 1, hjust = 0, size = 4, family = "serif") +
  theme_minimal()

Exercise 3

Using the titanic.rda dataset, recreate the following graphic as precisely as possible.

Hints:

• Create a new variable that uses died and survived as levels/categories
• Hex colors: #D55E00D0, #0072B2D0 (no alpha is being used)

Code

titanic1 <- titanic %>%
  mutate(mortality = as_factor(ifelse(survived == 0, "died", "survived"))) %>%
  select(!survived)

colors <- c("#D55E00D0", "#0072B2D0")

ggplot(titanic1, aes(sex, fill = sex)) +
  geom_bar(show.legend = FALSE) +
  labs(x = NULL) +
  facet_grid(fct_rev(mortality) ~ class) +
  theme_minimal() +
  scale_fill_manual(values = colors)

Exercise 4

Use the athletes_dat dataset — extracted from Aus_althetes.rda — to recreate the following graphic as precisely as possible. Create the graphic twice: once using patchwork and once using cowplot.

Code

# Get list of sports played by BOTH sexes
both_sports <- Aus_athletes %>%
  # dataset of columns sex and sport 
  # only unique observations
  distinct(sex, sport) %>%
  # see if sport is played by one gender or both
  count(sport) %>%
  # only want sports played by BOTH sexes
  filter(n == 2) %>%
  # get list of sports
  pull(sport)

# Process data
athletes_dat <- Aus_athletes %>%
  # only keep sports played by BOTH sexes
  filter(sport %in% both_sports) %>%
  # rename track (400m) and track (sprint) to be track
  # case_when will be very useful with shiny apps
  mutate(
    sport = case_when(
      sport == "track (400m)" ~ "track",
      sport == "track (sprint)" ~ "track",
      TRUE ~ sport
      )
    )

Hints:

• Build each plot separately
• Bar plot: lower limit 0, upper limit 95
• Bar plot: shift bar labels by 5 units and top justify
• Bar plot: label size is 5
• Bar plot: #D55E00D0 & #0072B2D0 — no alpha
• Scatterplot: #D55E00D0 & #0072B2D0 — no alpha
• Scatterplot: filled circle with “white” outline; size is 3
• Scatterplot: rcc is red blood cell count; wcc is white blood cell count
• Boxplot: outline #D55E00 and #0072B2; shading #D55E0040 and #0072B240
• Boxplot: should be made narrower; 0.5
• Boxplot: Legend is in top-right corner of bottom plot
• Boxplot: Space out labels c("female ", "male")
• Boxplot: Legend shading matches hex values for top two plots

Using patchwork

Code

#rename f/m -> female/male
levels(athletes_dat$sex) <- list(female = "f", male = "m")

#colors
colors <- c("#D55E00D0", "#0072B2D0")

#label
label1 <- athletes_dat %>%
  group_by(sex) %>%
  summarize(n = n())

#barplot
bar <- ggplot(athletes_dat, aes(sex)) +
  geom_bar(fill = colors) +
  ylim(0, 95) +
  theme_minimal() +
  labs(x = NULL,
       y = "number") +
  geom_text(
    data = label1,
    aes(label = n, y = n),
    vjust = "top",
    nudge_y = -5,
    size = 5
  )

#scatterplot
scatter <- ggplot(athletes_dat, aes(x = rcc, y = wcc, fill = sex)) +
  geom_point(
    show.legend = FALSE,
    shape = 21,
    color = "white",
    size = 3
  ) +
  theme_minimal() +
  scale_fill_manual(values = c("#D55E00D0", "#0072B2D0")) +
  labs(x = "RBC count", y = "WBC count")


#boxplot
box <- ggplot(athletes_dat, aes(sport, pcBfat, fill = sex, color = sex)) +
  geom_boxplot(width = .5) +
  scale_fill_manual(values = c("#D55E0040", "#0072B240"),
                    labels = c("female    ", "male")) +
  scale_color_manual(values = c("#D55E00", "#0072B2"),
                     labels = c("female    ", "male")) +
  theme_minimal() +
  labs(x = NULL,
       y = "% body fat",
       fill = NULL,
       color = NULL) +
  theme(
    legend.position = c(1, 1),
    legend.justification = c(1, 1),
    legend.direction = "horizontal"
  ) +
  guides(color = guide_legend(override.aes = list(
    fill = c(colors), color = NA
  )))
 

#arrange plots
(bar | scatter) / box 

Using cowplot

Use cowplot::plot_grid() to combine them.

Code

top_row <- plot_grid(bar, scatter)
plot_grid(top_row, box, ncol = 1)

Exercise 5

Create the following graphic using patchwork.

Hints:

• Use plots created in Exercise 4
• inset theme is classic
  • Useful values: 0, 0.45, 0.75, 1
• plot annotation "A"

Code

inset <- inset_element(bar, left = 0.75, right = 1, bottom = 0, top = 0.45) & theme_classic()
scatter + inset + plot_annotation(tag_levels = "A")